Cyberattacks represent an ever-growing threat that has become a real priority for most organizations. Attackers use sophisticated attack scenarios to deceive defense systems in order to access private data or cause harm. Machine Learning (ML) and Deep Learning (DL) have demonstrate impressive results for detecting cyberattacks due to their ability to learn generalizable patterns from flat data. However, flat data fail to capture the structural behavior of attacks, which is essential for effective detection. Contrarily, graph structures provide a more robust and abstract view of a system that is difficult for attackers to evade. Recently, Graph Neural Networks (GNNs) have become successful in learning useful representations from the semantic provided by graph-structured data. Intrusions have been detected for years using graphs such as network flow graphs or provenance graphs, and learning representations from these structures can help models understand the structural patterns of attacks, in addition to traditional features. In this survey, we focus on the applications of graph representation learning to the detection of network-based and host-based intrusions, with special attention to GNN methods. For both network and host levels, we present the graph data structures that can be leveraged and we comprehensively review the state-of-the-art papers along with the used datasets. Our analysis reveals that GNNs are particularly efficient in cybersecurity, since they can learn effective representations without requiring any external domain knowledge. We also evaluate the robustness of these techniques based on adversarial attacks. Finally, we discuss the strengths and weaknesses of GNN-based intrusion detection and identify future research directions.

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. Recently, the application of Graph Representation Learning (GRL) techniques on graph-structured data has demonstrated impressive capabilities in malware detection. This success benefits notably from the robust structure of graphs, which are challenging for attackers to alter, and their intrinsic explainability capabilities. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures such as Function Call Graphs (FCGs) and Control Flow Graphs (CFGs). This study also discusses the robustness of GRL-based methods to adversarial attacks, contrasts their effectiveness with other ML/DL approaches, and outlines future research for practical deployment.

To this day, abstention rates continue to rise, largely due to the need to travel to vote. This is why remote e-voting will increase the turnout by allowing everyone to vote without the need to travel. It will also minimize the risks and obtain results in a faster way compared to a traditional vote with paper ballots. In fact, given the high stakes of an election, a remote e-voting solution must meet the highest standards of security, reliability, and transparency to gain the trust of citizens. In literature, several remote e-voting solutions based on blockchain technology have been proposed. Indeed, the blockchain technology is proposed today as a new technical infrastructure for several types of IT applications because it allows to remove the TTP and decentralize transactions while offering a transparent and fully protected data storage. In addition, it allows to implement in its environment the smart-contracts technology which is used to automate and execute agreements between users. In this paper, we are interested in reviewing the most revealing e-voting solutions based on blockchain technology.

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.

Since the emergence of the Bitcoin cryptocurrency, the blockchain technology has become the new Internet tool with which researchers claim to be able to solve any existing online problem. From immutable log ledger applications to authorisation systems applications, the current technological consensus implies that most of Internet problems could be effectively solved by deploying some form of blockchain environment. Regardless this ‘consensus’, there are decentralised Internet-based applications on which blockchain technology can actually solve several problems and improve the functionality of these applications. The development of these new blockchain-based solutions is grouped into a new paradigm called Blockchain 3.0 and its concepts go far beyond the well-known cryptocurrencies. In this paper, we study the current trends in the application of blockchain on the paradigm of Public Key Infrastructures (PKI). In particular, we focus on how these current trends can guide the exploration of a fully Decentralised Identity System, with blockchain as be part of the core technology.